Interactive system for similarity-based inspection and assessment of the well-being of mHealth users
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-252333
- Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition ofRecent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.…
Autor(en): | Subash Prakash, Vishnu Unnikrishnan, Rüdiger Pryss, Robin Kraft, Johannes Schobel, Ronny Hannemann, Berthold Langguth, Winfried Schlee, Myra Spiliopoulou |
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URN: | urn:nbn:de:bvb:20-opus-252333 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Medizinische Fakultät / Institut für Klinische Epidemiologie und Biometrie |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Entropy |
ISSN: | 1099-4300 |
Erscheinungsjahr: | 2021 |
Band / Jahrgang: | 23 |
Heft / Ausgabe: | 12 |
Aufsatznummer: | 1695 |
Originalveröffentlichung / Quelle: | Entropy (2021) 23:12, 1695. https://doi.org/10.3390/e23121695 |
DOI: | https://doi.org/10.3390/e23121695 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit | |
Freie Schlagwort(e): | condition prediction; ecological momentary assessment; medical analytics; time series; visual analytics |
Datum der Freischaltung: | 26.05.2023 |
Datum der Erstveröffentlichung: | 17.12.2021 |
EU-Projektnummer / Contract (GA) number: | 761307 |
OpenAIRE: | OpenAIRE |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |